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# Courses | ||
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## Algorithms | ||
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- [Algorithms at Coursera by Wayne and Sedgewick](https://www.coursera.org/course/algs4partI) | ||
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## Datacamp | ||
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- [Recommended Courses by Datacamp](https://www.datacamp.com/courses/) | ||
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## Dataiku | ||
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- [Dataiku Teachable](http://dataiku.teachable.com/courses) | ||
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## Data Science | ||
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- [Data Science Primer](https://elitedatascience.com/primer) | ||
- [Coursera course: Getting and Cleaning Data](https://www.coursera.org/learn/data-cleaning?recoOrder=20&utm_medium=email&utm_source=recommendations&utm_campaign=u0faoCsqEemEkbug8nMVQQ) | ||
- [Data Science courses on Coursera](https://www.coursera.org/learn/competitive-data-science) | ||
- [Data courses on Udemy](https://www.udemy.com/courses/search/?ref=home&src=ukw&q=data) | ||
- [Data courses on Udacity](https://eu.udacity.com/courses/school-of-data-science) | ||
- [Latest Machine learning, visualization, data mining techniques. Online Masters in Data Analytic from Penn State](https://twitter.com/analyticbridge/status/1102667686302179336) | ||
- [Coursera Course: Probability and distribution](https://media.licdn.com/dms/document/C511FAQGFKgIKuW_EEA/feedshare-document-pdf-analyzed/0?e=1571785200&v=beta&t=XyEEqUgi3y4L1hiZ7CxlxbAXyZmM_zcCCdn-Lr04ns8) [deadlink] | ||
- [Coursera Data Science Methodology course](https://www.coursera.org/learn/data-science-methodology?aid=true) | ||
- From Problem to Approach and From Requirements to Collection | ||
- Business Understanding | ||
- Analytic Approach | ||
- Data Requirements | ||
- Data Collection | ||
- From Understanding to Preparation and From Modeling to Evaluation | ||
- Data Understanding | ||
- Data Preparation | ||
- Modeling | ||
- Model Evaluation | ||
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## Computer Vision | ||
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- [Introduction to Computer Vision, Udacity, GeorgiaTech](https://www.udacity.com/course/introduction-to-computer-vision--ud810) (free, paid for certification) | ||
- [Stanford Computer Vision Lab : Teaching](http://vision.stanford.edu/teaching.html) - Contains publications other than courses (free) | ||
- [Introduction to CV, IBM](https://www.coursera.org/learn/introduction-computer-vision-watson-opencv) (free, paid for certification) | ||
- [Convolutional Neural Networks, Coursera](https://www.coursera.org/learn/convolutional-neural-networks) (free, paid for certification) | ||
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### Image Processing | ||
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- [Image and Video Processing course by Duke University, Coursera](https://www.coursera.org/learn/image-processing) (free, paid for certification) | ||
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## Fast.ai | ||
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- [Practical Deep Learning for Coders, v3](https://course.fast.ai/) | ||
- [Part 2: Deep Learning from the Foundations](https://course.fast.ai/part2) | ||
- [Introduction to Machine Learning for Coders](http://course18.fast.ai/ml) | ||
- [Computational Linear Algebra](https://github.com/fastai/numerical-linear-algebra/blob/master/README.md) | ||
- [Code-First Introduction to Natural Language Processing](https://www.fast.ai/2019/07/08/fastai-nlp/) | ||
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## Intel | ||
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- [Intel® AI Courses](https://software.intel.com/en-us/ai/courses) | ||
- [Featured Course: AI from the Data Center to the Edge – An Optimized Path using Intel® Architecture](https://software.seek.intel.com/DataCenter_to_Edge_REG) | ||
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### FPGA | ||
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- [Intel AI Developer Program - Deep Learning Inference With Intel® FPGAs](https://software.intel.com/en-us/ai/courses/deep-learning-inference-fpga) | ||
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## Machine Learning | ||
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- ML course by [Weights & Biases | WandB](https://wandb.com) | ||
- [Code from the class](https://github.com/lukas/ml-class) | ||
- [Setup Instructions](https://github.com/lukas/ml-class) | ||
- [Slides](https://storage.googleapis.com/wandb/Bloomberg%20Class%201.pdf) | ||
- [Building and Debugging CNNs](https://wb-ml.slack.com/files/UN2SL6G7Q/FNE9193U0/bloomberg_class_2.pdf) | ||
- [Introduction to ML](https://wb-ml.slack.com/files/UN2SL6G7Q/FNE3Q7NN7/bloomberg_class_3.pdf) | ||
- [Course material by Students of AI (Imperial College, London)](https://github.com/Students-for-AI/The-Academy-of-AI) | ||
- [Comprehensive list of machine learning videos by Yaz](https://github.com/yazdotai/machine-learning-video-courses) | ||
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### Java/JVM | ||
- [ML for Java Developers Course](http://numahub.com/courses/machine-learning-java-developers) | ||
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### Deep Learning | ||
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- [Code examples for the Stanford's course: TensorFlow for Deep Learning Research](https://github.com/chiphuyen/stanford-tensorflow-tutorials) | ||
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#### Reinforcement Learning | ||
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- Reinforcement Learning Crash Course by Central London Data Science meetup - [GitHub repo](https://github.com/central-ldn-data-sci/CrashCourseRL) | [Slides](https://github.com/central-ldn-data-sci/CrashCourseRL/blob/master/Crash%20Course%20in%20Reinforcement%20Learning.pdf) | Notebooks: [1](https://github.com/central-ldn-data-sci/CrashCourseRL/blob/master/CrashCourseRL.ipynb) | [2](https://github.com/central-ldn-data-sci/CrashCourseRL/blob/master/crash_course_reinforcement_learning.ipynb) | [3](https://www.kaggle.com/blairyoung/crash-course-in-reinforcement-learning) | ||
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## Natural Language Processing (NLP) | ||
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- [How to Get Started with Deep Learning for Natural Language Processing (7-Day Mini-Course)](https://machinelearningmastery.com/crash-course-deep-learning-natural-language-processing/) | ||
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## Python: Best practices | ||
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- [Pluralsight: Python Best Practices for Code Quality](https://www.pluralsight.com/courses/python-best-practices-code-quality) | ||
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## Python: Testing | ||
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- [Udemy Course: Automated Software Testing with Python](https://www.udemy.com/automated-software-testing-with-python/) | ||
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## Statistics | ||
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- Statistics courses at [Coursera](https://www.coursera.org/courses?query=statistics&) | ||
- [Udemy](https://www.udemy.com/courses/search/?src=ukw&q=statistics) | ||
- [Udacity](https://eu.udacity.com/courses/all) - search for `Statistics` | ||
- Harvard University: [Statistics 110](https://www.youtube.com/watch?v=KbB0FjPg0mw&list=PL2SOU6wwxB0uwwH80KTQ6ht66KWxbzTIo) | [more videos on their YouTube channel](https://www.youtube.com/user/Harvard/search?query=statistics) | ||
- [Stanford University](https://online.stanford.edu/courses?keywords=statistics) | ||
- [Statistical Inference [course]](https://www.coursera.org/learn/statistical-inference) | ||
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## Misc | ||
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- [Check out 50 most popular massive open online courses](https://www.onlinecoursereport.com/the-50-most-popular-moocs-of-all-time/) ([Tweet](https://twitter.com/java/status/984844161969983489)) | ||
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# Contributing | ||
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Contributions are very welcome, please share back with the wider community (and get credited for it)! | ||
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Please have a look at the [CONTRIBUTING](CONTRIBUTING.md) guidelines, also have a read about our [licensing](LICENSE.md) policy. | ||
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--- | ||
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Back to [main page (table of contents)](README.md) |
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